In 2016, Taylor's team published the first paper on ASIC Clouds. Taylor's group made the case that datacenters full of ASICs are in our near future, and showed a prototypical ASIC Cloud architecture, how they should be designed, and how they save TCO. They proposed Deep Learning ASIC Clouds before Google announced their TPU, and also proposed the use of video transcoding clouds for YouTube. If you read only one architecture paper this year, you should read the IEEE Micro 2017 Top Picks Issue ASIC Cloud Paper.

In 2017, Taylor's group published the first architecture paper on NRE, non-recurring engineering expense. They show how minimizing NRE can be more important for ASIC Cloud feasibility than optimizing accelerator speedup or energy efficiency. They present the first ever architect's model for NRE, using current industry parameters (paper) (youtube talk), and opening up a new area of research. With the rise of specialization and the end of Moore's law, driving down the cost of design will surely be an important driver of future research.

Taylor occasionally helps companies and other legal professionals evaluate their patent portfolios,
and provide advice to companies leveraging the Tilera TILE64 architecture. He has broad expertise in hardware and software, and on the Bitcoin cryptocurrency.
Taylor's research is funded primarily by the National Science Foundation (NSF), including the Secure and Trustworthy Cyberspace Program, and DARPA/MARCO's C-FAR, part of STARnet.

Between the gaps at school, Taylor worked on Apple's NuKernel microkernel, and co-wrote the first version
of Connectix Virtual PC, an x86-to-PowerPC dynamic translation engine, which was acquired by Microsoft. He also
contributed to the ChipWrights Visual Signal Processor in its
earliest stages.

Taylor received the NSF CAREER Award in 2009 and tenure in 2012.

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Paul G. Allen School of Computer Science & Engineering
University of Washington